Research Experiences for Undergraduates (REU)

2019 Electrical and Computer Engineering Opportunities

Name: Cristano Tapparello and Wendi Heinzelman

Department: Electrical and Computer Engineering

Research area: FMF CONNECT

Project Description

Fetal alcohol spectrum disorders (FASD) represent a major public health problem that affects up to 2 to 5 percent of school-aged children in the US. Unfortunately, only a small fraction of children with FASD and their families can access FASD-informed interventions due to significant systems- and family-level barriers.

In this project, we are currently working to develop and test a new mobile health intervention to help families raising children with FASD. Through an app on their smartphones, caregivers will be able to access information and tools that can help them learn new skills to manage their children’s behavior. They will also be able to connect with other caregivers for support and to share ideas. The student will be part of a diverse team, which includes engineers and clinical psychologists, and will be involved in the whole cycle of research and development, including software development and app content creation and refinement.

Name: Tom Howard
Department: Electrical and Computer Engineering
Research area: Robots for Rehabilitation
Lab website

Project Description

Probabilistic Models of Human-Robot Communication in Assistive and Rehabilitation Robotics: As the sophistication of tasks that assistive and rehabilitation robots are able to perform increases, so does the need for efficient and concise communication of information between the human and machine. Probabilistic approaches to human-robot communication show promise in their ability to reason about the uncertainty in the objectives conveyed by the person and in formation that is to be relayed by the machine.

This project will involve the design and implementation of a robot behavior on an assistive or rehabilitation robotics platform (e.g. opening a door, drinking from a cup, reacquiring range of motion and/or strength in a joint) and the implementation and training of models for communicating to/from the platform before, during, and after execution of the behavior.

Name: Gaurav Sharma
Department: Electrical and Computer Engineering
Lab website

Project Description #1

Assessing Disease Progression and Treatment Efficacy for Parkinson's and Huntington's Diseases Using Data Analytics on Body-Worn Sensors

Parkinson's and Huntington's diseases are characterized by debilitating motion irregularities: such as tremors, unsteady gain, involuntary movements, and lack of coordination. This project seeks to use analytics on data captures from minimally obtrusive sensors worn at multiple points on the body for detecting and classifying motion irregularities, for quantifying the durations of such symptoms, and for characterizing the efficacy of medication in mitigating these symptoms.

Project Description #2

The Full-Spectrum Mobile Experience: UR Color Barcodes

UR researchers have recently invented a color version of the seemingly ubiquitous mobile barcodes that allow us to layer three independent pieces of information within each barcode by using the "spectral diversity" afforded by color printing and capture. In this project, we are looking to develop an easy to use mobile application and to test how the capability for layering can be advantageously used for student activity flyers and other events around campus.

Project Description #3

Adaptive Color Visualization for Color Deficient Observers on Android Smartphones

Around 7-10% of the male population in North America has some form of color deficiency. These viewers often find it difficult to tell the difference between certain colors that appear clearly different to observers with normal color vision. The color deficiency is particularly problematic when it causes a loss of discriminability of different objects or when a color deficient individual must engage in a conversation involving standard color terminology designed for color normal viewers. As increasingly popular personalized imaging devices, smart phones can be used as tools to help color-deficient users overcome their deficiency. As a participant in this research you will help develop and deploy techniques for improved color visualization on Android smartphones by specifically exploiting the adaptivity and personalization that these devices offer.

Project Description #4

Noncoding RNA Gene Search: Unlock the hidden information in Genomes 

With the wide spread availability of high throughput sequencing technology, vast datasets of genomes are now available to researchers for exploration. Conventional protein coding genes can be located within these large genome data sets with relative ease using BLAST and other alignment tools. Noncoding RNAs (ncRNAs) that serve a direct functional role instead of providing a recipe for protein synthesis, however, present a challenge for genomic analysis. Across species ncRNAs are conserved in secondary structure rather than in sequence and they are therefore not discovered by common sequence alignment based search tools. With the discovery of an increasing number of ncRNAs it is clear that they represent the next frontier in advancing our understanding of the genomes. As a participant in this research, you will develop and evaluate new computational methods for identifying ncRNAs. 

Project Description #5

Principled Machine Learning Methods for Multiple Sequence Alignment

The alignment of sequence data is a fundamental task in analyzing genomic data, which shares several commonalities with string comparison. For aligning two sequences, hidden Markov models have emerged as the model of choice for aligning two sequences. For more than two sequences, several heuristics have been proposed that operate on sequences in a pair wise fashion. In this project, we seek an alternate more principled framework for multiple sequence alignment using principled machine learning methods. As a participant in this research, you will be involved in developing, implementing, and evaluating new techniques for multiple sequence alignment.